Evidence map›Paper›PMID 32208238›Full record

ArticleThe Science of the total environment2020

Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning.

Jinying Huang, Yun Zhu, James T Kelly, Carey Jang, Shuxiao Wang, Jia Xing, Pen-Chi Chiang, Shaojia Fan, Xuetao Zhao, Lian Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Science of the total environment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Spatial-Temporal Evolution of Health Impact and Economic Loss upon Exposure to PMInternational journal of environmental research and public health · 2022
    Article
  3. An Integration Method for Regional PMInternational journal of environmental research and public health · 2021
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. CABOT-OEnvironmental science & technology · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jinying HuangGuangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Yun ZhuGuangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Southern Marine Science and Engineering Guangdong Laboratory, Sun Yat-Sen University, Zhuhai 519000, China. Electronic address: zhuyun@scut.edu.cn.
James T KellyUS Environmental Protection Agency, Office Air Quality Planning & Standards, Research Triangle Park, NC 27711, USA.
Carey JangUS Environmental Protection Agency, Office Air Quality Planning & Standards, Research Triangle Park, NC 27711, USA.
Shuxiao WangState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
Jia XingState Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
Pen-Chi ChiangGraduate Institute of Environmental Engineering, National Taiwan University, Taipei 10673, Taiwan; Carbon Cycle Research Center, National Taiwan University, 10672, Taiwan.
Shaojia FanSouthern Marine Science and Engineering Guangdong Laboratory, Sun Yat-Sen University, Zhuhai 519000, China.
Xuetao ZhaoChinese Academy for Environmental Planning, Beijing 100012, China.
Lian YuGuangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of ~10

Indexed as

Air pollution control strategiesCost-benefit analysisGenetic algorithmMulti-pollutant optimizationOzonePM(2.5)

Identifiers

PMID32208238
PMCPMC7190429

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.